{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets.samples_generator import make_blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.数据加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PVLF69U46d85iqA5hVCplNNhVqxljeOLJWbz2+gKcTjuWZSgoyOHB311GQUFO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\n0WBXSqk0o8GulFJp5v8DKqhzsyzAUrsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = make_blobs(n_samples=100, centers=6, random_state=1234, cluster_std=0.6)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(x=x[:, 0], y=x[:, 1], c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.算法实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 引入scipy中的距离函数，默认欧式距离\n",
    "from scipy.spatial.distance import cdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class K_Means(object):\n",
    "    # 参数 n_clusters：k，max_iter 迭代次数，centroids 初始质心\n",
    "    def __init__(self, n_clusters=6, max_iter=300, centroids=[]):\n",
    "        self.n_clusters = n_clusters\n",
    "        self.max_iter = max_iter\n",
    "        self.centroids = np.array(centroids, dtype=np.float)\n",
    "\n",
    "    # 训练模型方法，k-means聚类过程  \n",
    "    def fit(self, data):\n",
    "        # 假如没有指定初始质心，就随机选取data中的点作为初始质心\n",
    "        if self.centroids.shape == (0,):\n",
    "            # 从data中随机生成0到data行的六个整数作为索引值\n",
    "            self.centroids = data[np.random.randint(0, data.shape[0], self.n_clusters), :]\n",
    "\n",
    "        # 开始迭代\n",
    "        for i in range(self.max_iter):\n",
    "            # 1.计算距离矩阵，得到的是一个100*6的矩阵\n",
    "            distances = cdist(XA=data, XB=self.centroids)\n",
    "            # 2.对距离按照由近到远排序，选取最近的质心点类别作为当前点的分类标签\n",
    "            c_ind = np.argmin(distances, axis=1)\n",
    "            # 3.对每一类数据进行均值计算，更新质心点坐标\n",
    "            for j in range(self.n_clusters):\n",
    "                # 排除掉没有出现在c_ind里的类别\n",
    "                if j in c_ind:\n",
    "                    # 选出所有类型是j的点，取出data里面坐标的均值，更新第j的质心\n",
    "                    self.centroids[j] = np.mean(data[c_ind == j], axis=0)\n",
    "\n",
    "    def predict(self, samples):\n",
    "        # 跟上面一样，先计算距离矩阵，然后选取距离最近的那个质心的类别\n",
    "        distances = cdist(samples, self.centroids)\n",
    "        c_ind = np.argmin(distances, axis=1)\n",
    "        return c_ind\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 0, 2], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist = np.array([[121, 543, 35, 234],\n",
    "                 [54, 35, 93, 23],\n",
    "                 [3, 75, 23, 43],\n",
    "                 [4, 5, 1, 3]\n",
    "                 ])\n",
    "c_ind = np.argmin(dist, axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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25tA3bhjf6n0zb+37BwqjcVGjGd0vo6enX8Qx3aanRXc5U92Z3Dr4Ucr8BQR1\ngHR377DGVYiWKPd7eWjtp00W+vNaQdaX5vHxvhwuyDr6bOkq0tISyOieyN59ZU22u1wms844+plY\nWmv27CnF4TDp2TNJslmIFthUuYKKYHFjMwr12byndgt7vVvJihvCJb1/wjv7nkChGu9mzsy4koyY\n7IhjxpixTEw984jnTnP35LYhjzW86q3jZnOXbki/LN6DUxkc+iY+rxXkw72bpCFtof/7zUXcfuds\nQiELvz+Ex+OkR0Y3vnP1tKMa7+tN+/njfe9QXe1Da016WgJ/uOdb9Osnd1qEOJy93q0EbF/YdssO\nsc+7jb5xwxiTfAoD4kfyddUKLB1iaMJ4Utw92qyGthxLdE0ri/fgNMywlee9VpA5ezdLQ9oCSin+\n7zcX8fO7XiJk2Y3Z3DMzmauvnHJUY27YuJd773+Pmpr6bM7onsjv7/kWfbPlOXEhDme3d0vEbNbY\n7K/bTlbcEE5KnsHA+NFsqlqBpS2GJU4k2dW9zWpI7eDZ3KUb0liHK2xFKqifxd0RFlToLAb0787L\nL/6ETxdsIj+/guHDezJt6iAcjtZPea6s9PLLX71KXd03S1Tv21/O7T+fzWsv/wy3W95nKERzkl3d\ncSp34/vGDjANJ90OWlwo3pnE5NSz27s8IVok1uGKuN1AkSjZ3GKDB/Vg9gv12VxYWMmIEb2YNnUQ\nptn6uyNl5bXc/evX8Pm+ucOzd18Zd/x8Nq++9DNZUV+Iw0hxdsepXE1evwL1U2oPzuYEZzKTU89p\n7/I6hC79X5CJ6VnEmA5qQ01/QGJMJ1cPGNeqsaoDPhbm52JrmxmZA0h2x7ZlqR1efHwMF1/Uur+z\nSOZ/+nXE51tCIZslS7cx83S5Mi5Ec0Ynncrc/BcJHnSlTaFwGi6GJU6MXmFCtMKU7tk4Iqzh4DId\nXDngpFaNVRXwsSh/O1rDjMwBJLk9bVVmp5CY6OHSS8Yf8zjz5m0MWzNCawgGLJav2M70U4ce8zmE\nOFGNTZ7B/MKXmywoqKhfa2FI4rH/7/NE0KUbUodh8OyMq/neopcINkwNCtgWt4w4hQnpfVo8ztx9\nOdyx/F1MZaABy7b5w/iz+Xb/lr08d2NZPu/s3kjAtji391CmdM/uss9llJTUEAiEwraHQiFKy2qi\nUJEQnYfHjOOGAffy2p5HKQsUoNH0iMnmyqw7cRgyu0B0Dk7D5NkZV3H9Z68QOiib7xw5g7GpvVo8\nzpy9m7lrxXuYygA0Ia25f8J5XNp3VIs+v74sj3d3bSSkbc7rM4xJ6VldOJurCQSssO0hy6a0tDYK\nFQnRecQ6Ehqy+W+UB4rQQGYewDmJAAAgAElEQVRMX67MugNTdelWrJE69HUd7WHChAl61apV7X7e\n5gRti+VFu6kO+pmcnkVqTFyLP1vqq2X6B4/js5o2UW7Twcfn3ERWfHIzn6z3781L+cfXiwlYITTg\nMZ2clzWchyae3yWDb/mK7dx7/7vU1TV9OXCM28lf/3INQ4dkRqkyITqXqmAZhjKaLBd/IlNKrdZa\nT4h2HZ1ZR8zmZYW7qAkFmNI9m5RWzDwq8dUw/YN/Nlm0EOqzef65P6ZnXLfDfv7xr7/gX5uXNGZz\njOnkkuyR3DfxvKP5Kp3eF0u28uDD74dls9vt4LFHr2XQwI79fJoQHYVkc2Qdb5mlKHAaJqf26M95\nfYa1qhkFmLt/S8SX2dq2zQd7Nh32s3m1lTy28XN8Vgib+jv5XivIh3s2sbpkX6vqOFFMnNCffn3T\ncbu/uWLkdjs46aRsaUaFaIVEZ0qXCTxxYnIaJtMzB3Ben2GtakYB5uzNifiaea01H+7dfNjP7q2p\n4IlNS5pkc50V5J3dG1lbur9VdZwopk4ZSFaf1CbPisa4nUyc0F+aUSFaQbI5MrlP3GBHVSlfVxTQ\nJy6JMSk9j3h30hcK8vTWFfxvy0rqrGDYfkvb+Bq2b60sZnN5IVnxyYxN/WbszwpyG5ZebjoNxmcF\n+WRfTqumDZ8oTNPgr3++hrfeWcW8+V9jmgbnnzuGC85v2fRnIYQQJ47cqhI2VRSSHZfMqJTMI2Zz\nXSjIUznLeX7bl2EzlwBCto0vVJ/NWyqKyKkoIjshuUnuL8rfTqTT+Kwgc/dtadW04ROFaRo8+tfv\n8PY7q5k7fyMOh8mF54/lvHPHRLs0IcQJoMs3pEHb4vZlb7MoPxeHMrC1JjshhRdOu6bZhYlsrfnO\notlsrigMmw50gNt0Mr3HAG74/FWWFe2qf760YewXT/sOSW4PbsMRMVxNZRDj6LrPe7lcDq66YgpX\nXXF0S9MLIYTo3AKWxS3L3uKLgh2N2dw/MZXnZlzT7MJElm1z9YIX2FpZjN+OnM1OwyDZ7eH8j58i\nt7oEp6pfPKl/YirPn3YN3Vwe3KYDI8L9VVMZeMyum81ut5OrrpzCVUf52hghhGhOl5+y+1TOchbl\n5+KzQtSEAnitINsri7l75QfNfmZxwQ62VhY124x6TCeX9h3F4oIdLC3chc8KUXtg7KpifvPlhwDM\n6jWYSM/wOgyDS7LlHahCCCG6pn9tXsIXBTuaZPOWyqLG/Izks4JccqtLm21GHQ0vg7/vq3nkVBYR\ntG28VhCvFSSnoojfrZoDwJm9BmNHeCmcwzC4MHtEG3w7IYQQB+vyDelLuWvCpvUEtc3nBbl4D3kd\nzAHry/LwhsKn6QKMSenJ09Ov5I/jz+HVHV+FBWPQtpm7fwsL87aT4HTzj2mX4jGdxDlcxDpcuA0H\nvx47iwGJXedF03n5FfznqYX88b53+GjOOvz+yH+3XYm2y9ChXLRVEu1ShBCi3b2S+1V4Nts2C/K3\nNXsx+KuS/RFzWwFp7jhMZeC3LYI6wqvFtM2HezezuCCXJJeHR6dcQozpaMhmJy7D5LcnnUXfhJQ2\n+X6dwf795fz7yQX88b53mPOxZLMQ4vjp8lN2fc00llA/ZSg2wt9QhicBj+kMe3Y01uHk2oHjmdw9\nu37sZkJTA7csfYtZvQbxtymXsOzi21iUv52gZTE9cwBprVxYqTP7ctVO/t/v38SybEIhm+Urcnnl\ntRU88fh3iY+LiXZ57U77V6BrHoPgOlBO0EG0YzAq/hZUzOnRLk8IIdqFL8LaDFD/7suAbeE2w8M5\nMzZyNrtNB1VBHwE7/LUlh/rJF29yTu8h/GnyRSy/6DYW5ucSsi1mZA5o9aKHndmKlbn8/o9vEwrZ\nWFZ9Nr/6+kqe+Md3iY11R7s8IcQJpsvfIT2950DMCM9xZsenNPucynl9huE0mv7VKcBlmJzT55uX\nQ5+eGXlsqF+x79P921hSuJMEp5sLs0bwrX6ju1Qzatuahx5+H78/RChUf8Xa5wtSVFjJ66+vxLY1\ny1ds5x+Pz+P5F5dQWFQZ5YqPL9v7Krr8Rgh+CQRA19b/39BGdMXt2DVPRLtEIYRoF6c1k58DE9NI\ncEZuiC7IGoEZIZtDtt34rvEjqbOCfLJvCyuK95DgiuGi7Pps7krNqGXZPPTwB/j9ISzrm2wuKKjk\nzbdWYVk2S5dt4x+Pz+PF2UspKqqKcsVCiM6uTe6QKqWSgP8CI6m/AfgDrfWythj7eLtr9OksLthB\nddCPzwrhMkwchsHDky5o9jPxTjcvz7yOW5e+zT5vJWjIik/isWnfItbhajzu7jEzWVq0i0p/XcQp\nQl4ryJy9OZzSo/9x+W7tTWvNhx+t5Y23VlFT42PihP5c/71T6d49MeLxe/eWUucLvwoeCFos+mwz\nG7/eT05OHnW+IE6nwcuvLOP//fYSpk4ZeLy/SrvTwW1QdT/ga+aIOqj5N9o1EeWa2J6lCSE6qc6c\nzb8cM5MlhTupDQUas9lpmDw46fxmP5PoiuHl06/l1mVvk+etAg19E5LZU1NByArP4ObUWUE+2ZvD\nlIbZTp2dbWs++HAtb771JbVeP5Mm9uf6708nPS0h4vG7dpcQCIbP8AoEQixctInVX+1i29aChmw2\nmf3yUv5wz7eYNPHE+F1GCNH+2mrK7t+Bj7XWlyulXEDrXhgWRRmeBOad+2Ne37mOVSV7GZCQxjUD\nx5EZG7mJOmBoUgZzz/sxebWVoBQ9Dzp+Yd42nsxZTrGvhjN6DqIy4GP+/q2EDmlKDRSeE2g13cef\nmM+cj9fja2gy583fyLLl23n2vzeQnBx+dTnG48S2wxeOgPqmdHNOXuNYwaAN2Dzw4Pu89catOJ3m\ncfse0aC9zwBHej7Hh675DypFGlIhRIt02mzOjE1k/nk/4bUda1lTuo+BiWlcM2AcPY6QzcOTezDv\n3B+T563CUIrM2ERGvPFwq85tKHVCrXT/2D/m8sm8Dfj99U3mvPkbWb4il2f/ewPduoX/SMTENJ/N\n9XdK8/E1jBUM1t95vu+B93jr9VtwOE6sbBZCtI9jbkiVUonAdOD7AFrrABB5NaAOKsEVww+GTOYH\nQya3+rM947qhtWZF0W7WluaRU1HI3H1b8DUsZrS3poIEpxunYYZdoXWZJpf1Hd0m3yHaystr+fDD\ntQSC30yLsm1NXV2At99dzQ++Pz3sMxndu5GdnUpublGT8IuJceJ2OfH5wqcBaTSbc/IYPeoEe0er\nbx6Hvo82osAStNZHfBefEKJrOxGyOdEVww1DW/+KEaUUvRqyeXnRbhKc7mbXdIjEaZgnzEr3JSXV\nfDx3PYHAN/liWRqv1887763he9edEvaZXj2T6dUrmZ07S5q8CSAmxonD6WhsRg9m2zZbthYwYnjX\ne0erEOLYtcUzpP2BYuBZpdRXSqn/KqXCbocppW5SSq1SSq0qLi5ug9N2DH4rxLWLZnPD56/y1w2L\neG/P143NKNSv3FcTCjC9R39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fkK3bCliwcBNnnTnqmMZvS18Uv0dIH74BD2o/nxW9JQ2p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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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25tA3bhjf6n0zb+37BwqjcVGjGd0vo6enX8Qx3aanRXc5U92Z3Dr4Ucr8BQR1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\neG/P143NKNSv3FcTCjC9R39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fkK3bCliwcBNnnTnqmMZvS18Uv0dIH74BD2o/nxW9JQ2p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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义一个绘制子图函数\n",
    "def plotKMeans(x, y, centroids, subplot, title):\n",
    "    # 分配子图, 121表示1行2列中的第一个\n",
    "    plt.subplot(subplot)\n",
    "    plt.scatter(x[:, 0], x[:, 1], c='r')\n",
    "    # 画出质心点\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1], c=np.array(range(centroids.shape[0])), s=100)\n",
    "    plt.title(title)\n",
    "\n",
    "kmeans = K_Means(max_iter=300, centroids=np.array([[2, 1], [2, 2], [2, 3], [2, 4], [2, 5], [2, 6]]))\n",
    "plt.figure(figsize=(16, 6))\n",
    "plotKMeans(x, y, kmeans.centroids, 121, 'Initial State')\n",
    "\n",
    "# 开始聚类\n",
    "kmeans.fit(x)\n",
    "plotKMeans(x, y, kmeans.centroids, 122, 'Final State')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
